The code, the documentation and example datasets are available open-source at www.msstats.org under the Artistic-2.0 license. The package can be downloaded from www.msstats.org or from Bioconductor www.bioconductor.org and used in an R command line workflow. The package can also be accessed as an external tool in Skyline (Broudy et al., 2014) and used via graphical user interface.

Targeted mass spectrometry by selected reaction monitoring (S/MRM) has proven to be a suitable technique for the consistent and reproducible quantification of proteins across multiple biological samples and a wide dynamic range. This performance profile is an important prerequisite for systems biology and biomedical research. However, the method is limited to the measurements of a few hundred peptides per LC-MS analysis. Recently, we introduced SWATH-MS, a combination of data independent acquisition and targeted data analysis that vastly extends the number of peptides/proteins quantified per sample, while maintaining the favorable performance profile of S/MRM. Here we applied the SWATH-MS technique to quantify changes over time in a large fraction of the proteome expressed in Saccharomyces cerevisiae in response to osmotic stress.We sampled cell cultures in biological triplicates at six time points following the application of osmotic stress and acquired single injection data independent acquisition data sets on a high-resolution 5600 tripleTOF instrument operated in SWATH mode. Proteins were quantified by the targeted extraction and integration of transition signal groups from the SWATH-MS datasets for peptides that are proteotypic for specific yeast proteins. We consistently identified and quantified more than 15,000 peptides and 2500 proteins across the 18 samples. We demonstrate high reproducibility between technical and biological replicates across all time points and protein abundances. In addition, we show that the abundance of hundreds of proteins was significantly regulated upon osmotic shock, and pathway enrichment analysis revealed that the proteins reacting to osmotic shock are mainly involved in the carbohydrate and amino acid metabolism. Overall, this study demonstrates the ability of SWATH-MS to efficiently generate reproducible, consistent, and quantitatively accurate measurements of a large fraction of a proteome across multiple samples. In systems biology and biomedical studies targeted mass spectrometry via selected reaction monitoring (SRM) 1 (also known as multiple reaction monitoring, MRM) has emerged as a powerful technique for the consistent and reproducible quantification of proteins across numerous complex samples (1-6). Optimal sets of precursor/fragment ion pairs, called transitions, uniquely represent a specific peptide. They constitute a definitive mass spectrometric assay for the detection of targeted peptides, and thus the proteins from which they derive, in the complex matrix of trypsinized biological samples (1, 7). Protein quantification is then performed by relating the intensity of the acquired transition signals to suitable reference signals. Most quantification strategies commonly used in proteomics are compatible with this method (8). Recently, the high-throughput development of S/MRM assays has been achieved via the generation of MS/MS spectral libraries from the measurements of thousands of synthetic peptides representing proteotypic peptides (9). Moreover, many experime...

Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a standalone tool or in integration with the existing computational pipelines. Molecular & Cellular Proteomics 11: 10.1074/ mcp.M111.014662, 1-12, 2012.Selected reaction monitoring (SRM) 1 is a mass spectrometry technique that can accurately and reproducibly quantify proteins in complex biological mixtures (1, 2, 3). It can cover a nearly complete dynamic range of abundance of cellular proteome, with a lower boundary of detection below 50 copies per cell for single cellular organisms (3). Considerable efforts are currently invested into developing high-throughput SRM assays, even for whole proteomes (4, 5). These assays are then used to simultaneously quantify hundreds of proteins with a high degree of reproducibility across multiple samples, and as a result the assays are increasingly used in systems biology and in clinical investigations (3,6,7,8).SRM experiments quantify a priori known protein species. They require knowledge of the peptides of these proteins that are unique to the target proteins and can be observed by a mass spectrometer (9, 10), and of the mass spectrometric characteristics of these peptides such as fragment ion mass, signal intensity distribution, and optimal collision energy (2). Enzymatically digested proteins are subjected to liquid chromatography separation and are monitored in a triple quadrupole mass spectrometer, and the ion signals for an a prior...

We present a computationally efficient algorithm for the eigenspace decomposition of correlated images. Our approach is motivated by the fact that for a planar rotation of a twodimensional image, analytical expressions can be given for the eigendecomposition, based on the theory of circulant matrices. These analytical expressions turn out to be good first approximations of the eigendecomposition, even for three-dimensional objects rotated about a single axis. We use this observation to automatically determine the dimension of the subspace required to represent an image with a guaranteed user-specified accuracy, as well as to quickly compute a basis for the subspace. Examples show that -the algorithm performs very well on a range of test images composed of three-dimensional objects rotated about a single axis.